scholarly journals Detection of Different Hosts From a Distance Alters the Behaviour and Bioelectrical Activity of Cuscuta racemosa

2021 ◽  
Vol 12 ◽  
Author(s):  
André Geremia Parise ◽  
Gabriela Niemeyer Reissig ◽  
Luis Felipe Basso ◽  
Luiz Gustavo Schultz Senko ◽  
Thiago Francisco de Carvalho Oliveira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Host Plants ◽  
Parasitic Plants ◽  
Machine Learning Techniques ◽  
Bioelectrical Activity ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
Ecological Aspects ◽  
Electrical Signalling ◽  
Β Carotene

In our study, we investigated some physiological and ecological aspects of the life of Cuscuta racemosa Mart. (Convolvulaceae) plants with the hypothesis that they recognise different hosts at a distance from them, and they change their survival strategy depending on what they detect. We also hypothesised that, as an attempt of prolonging their survival through photosynthesis, the synthesis of chlorophylls (a phenomenon not completely explained in these parasitic plants) would be increased if the plants don’t detect a host. We quantified the pigments related to photosynthesis in different treatments and employed techniques such as electrophysiological time series recording, analyses of the complexity of the obtained signals, and machine learning classification to test our hypotheses. The results demonstrate that the absence of a host increases the amounts of chlorophyll a, chlorophyll b, and β-carotene in these plants, and the content varied depending on the host presented. Besides, the electrical signalling of dodders changes according to the species of host perceived in patterns detectable by machine learning techniques, suggesting that they recognise from a distance different host species. Our results indicate that electrical signalling might underpin important processes such as foraging in plants. Finally, we found evidence for a likely process of attention in the dodders toward the host plants. This is probably to be the first empirical evidence for attention in plants and has important implications on plant cognition studies.

scholarly journals ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 499 ◽  
Author(s):  
Iqbal H. Sarker ◽  
Yoosef B. Abushark ◽  
Asif Irshad Khan
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Model Complexity ◽  
Context Aware ◽  
Smartphone Apps ◽  
Data Set ◽  
Machine Learning Classification ◽  
Learning Techniques

This paper mainly formulates the problem of predicting context-aware smartphone apps usage based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the user-centric context and device-centric context. In the area of artificial intelligence and machine learning, decision tree model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain higher dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the prediction accuracy of the context-aware model. In order to address these issues, in this paper, we present an effective principal component analysis (PCA) based context-aware smartphone apps prediction model, “ContextPCA” using decision tree machine learning classification technique. PCA is an unsupervised machine learning technique that can be used to separate symmetric and asymmetric components, and has been adopted in our “ContextPCA” model, in order to reduce the context dimensions of the original data set. The experimental results on smartphone apps usage datasets show that “ContextPCA” model effectively predicts context-aware smartphone apps in terms of precision, recall, f-score and ROC values in various test cases.

scholarly journals Student Intervention System using Machine Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2061-2065
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Student Records ◽  
Teaching Methodologies ◽  
Machine Learning Classification ◽  
Classification Technique ◽  
Learning Techniques ◽  
Vector Machines

Now a days, the educational institutes are adopting technologies for betterment of student’s quality, in respect to teaching methodologies etc. For which the huge information available with educational institutes can be used to predict student’s future in academics. The main objective of this paper is to predict the student performance in the examination and also to predict the student will graduate or not. Hence forth we are using statistical analytical method which is F1 score. F1 score or F measure is used to test the prediction accuracy by considering precision and recall to compute the score. To fulfill this requirement in machine learning, classification technique is used. The dataset used in this analysis contains 395 student records, having attributes, such as age, health, internet, school, father job, mother job etc. Using support vector machines (SVM), Decision Tree and Naïve Bayes (NB) classification algorithms F1 score is calculated for each algorithm. Based on the analysis done the F1 score of support vector machine is giving the better prediction compared to rest of the two algorithms.

scholarly journals Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5438 ◽  
Author(s):  
Valentín Barral ◽  
Carlos J. Escudero ◽  
José A. García-Naya ◽  
Pedro Suárez-Casal
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Low Cost ◽  
Ultra Wideband ◽  
Machine Learning Techniques ◽  
Positioning Systems ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
Positioning Algorithm ◽  
Non Line Of Sight

Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation.

scholarly journals Performance Evaluation of Machine Learning Techniques for DOS Detection in Wireless Sensor Network

2021 ◽  
Vol 13 (2) ◽  
pp. 21-29
Author(s):  
Lama Alsulaiman ◽  
Saad Al-Ahmadi
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Denial Of Service ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Security Threats ◽  
Dos Attacks ◽  
Machine Learning Classification ◽  
Learning Techniques

The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of Service (DoS) attacks. Machine learning classification techniques have been used as an approach for DoS detection. This paper conducted an experiment using Waikato Environment for Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS attacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The results showed that the random forest classifier outperforms the other classifiers with an accuracy of 99.72%.

scholarly journals Performance and Computation Time Enhancement of Various Machine Learning Techniques for NSL-KDD Dataset

2020 ◽  
Vol 8 (6) ◽  
pp. 4726-4730
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Computation Time ◽  
Machine Learning Techniques ◽  
Huge Number ◽  
Machine Learning Classification ◽  
Learning Techniques

To develop an effective intrusion detection system we definitely need a standardize dataset with a huge number of correct instances without missing values. This would significantly help the system to train and test for real-time use. Previously for research purpose, KDD-CUP’99 dataset has been used, but later on, it has been seen that it is not so useful for training the model as it consists a lot of missing and abundant values. All this issue have been tackled in NSL dataset. To analyze the capabilities of the dataset for intrusion detection system we have analyzed various machine learning classification algorithm to classify the attack over any network. This paper has explored many facts about the dataset and the computation time.

scholarly journals COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms

2021 ◽  
Vol 1 (2) ◽  
pp. 100-105
Author(s):  
Nasiba M. Abdulkareem ◽  
Adnan Mohsin Abdulazeez ◽  
Diyar Qader Zeebaree ◽  
Dathar A. Hasan
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Vaccine Development ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Real World Data ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
And Performance

In December 2019, SARS-CoV-2 caused coronavirus disease (COVID-19) distributed to all countries, infecting thousands of people and causing deaths. COVID-19 induces mild sickness in most cases, although it may render some people very ill. Therefore, vaccines are in various phases of clinical progress, and some of them being approved for national use. The current state reveals that there is a critical need for a quick and timely solution to the Covid-19 vaccine development. Non-clinical methods such as data mining and machine learning techniques may help do this. This study will focus on the COVID-19 World Vaccination Progress using Machine learning classification Algorithms. The findings of the paper show which algorithm is better for a given dataset. Weka is used to run tests on real-world data, and four output classification algorithms (Decision Tree, K-nearest neighbors, Random Tree, and Naive Bayes) are used to analyze and draw conclusions. The comparison is based on accuracy and performance period, and it was discovered that the Decision Tree outperforms other algorithms in terms of time and accuracy.

scholarly journals On predicting academic performance with process mining in learning analytics

2017 ◽  
Vol 10 (2) ◽  
pp. 160-176 ◽  
Author(s):  
Rahila Umer ◽  
Teo Susnjak ◽  
Anuradha Mathrani ◽  
Suriadi Suriadi
Keyword(s):  
Machine Learning ◽  
Process Mining ◽  
Learning Experience ◽  
Dropout Rate ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
The Impact

Purpose The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques. Design/methodology/approach Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used. Findings The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way. Practical implications Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate. Social implications Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course. Originality/value This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

scholarly journals Efficient Cognitive Fog Computing for Classification of Network Cyberattacks Using Machine Learning

2020 ◽  
pp. 176-184
Author(s):  
A. V. Deorankar ◽  
Shiwani S. Thakare
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Binary Classification ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Machine Learning Classification ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Iot Devices ◽  
Cloud Servers

IoT is the network which connects and communicates with billions of devices through the internet and due to the massive use of IoT devices, the shared data between the devices or over the network is not confidential because of increasing growth of cyberattacks. The network traffic via loT systems is growing widely and introducing new cybersecurity challenges since these loT devices are connected to sensors that are directly connected to large-scale cloud servers. In order to reduce these cyberattacks, the developers need to raise new techniques for detecting infected loT devices. In this work, to control over this cyberattacks, the fog layer is introduced, to maintain the security of data on a cloud. Also the working of fog layer and different anomaly detection techniques to prevent the cyberattacks has been studied. The proposed AD-IoT can significantly detect malicious behavior using anomalies based on machine learning classification before distributing on a cloud layer. This work discusses the role of machine learning techniques for identifying the type of Cyberattacks. There are two ML techniques i.e. RF and MLP evaluated on the USNW-NB15 dataset. The accuracy and false alarm rate of the techniques are assessed, and the results revealed the superiority of the RF compared with MLP. The Accuracy measures by classifiers are 98 and 53 of RF and MLP respectively, which shows a huge difference and prove the RF as most efficient algorithm with binary classification as well as multi- classification.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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